Artificial Intelligence in 3D Printing: A Revolution in Health Care

2021 ◽  
pp. 57-79
Author(s):  
Aishwarya Banerjee ◽  
Haritha K. Haridas ◽  
Arunima SenGupta ◽  
Neetu Jabalia
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
3D Printing

scholarly journals Advanced technologies in the modern era for augmented patient health care and drug delivery

2020 ◽  
Vol 10 (1) ◽  
pp. 147-152
Author(s):  
Asish Dev ◽  
Soma Khanra ◽  
Nilay Shah
Keyword(s):  
Artificial Intelligence ◽  
Drug Delivery ◽  
Health Care ◽  
3D Printing ◽  
New Technology ◽  
Electrical Signal ◽  
Technological Advances ◽  
Patient Health ◽  
The Individual ◽  
Personalized Medicines

The objective of the work is to recognize the recent advancements in the modern health care and drug delivery systems. The article describes few recent developments in technology like artificial intelligence, personalized medicines, customized medicines, 3D printing, bioelectronic devices and tele pharmacy, which have the potential to augment health care and drug delivery in coming times. Personalized medication ensures precise health care as per the individual genetic makeup of the patients. The 3D printing technology enables to deliver tailor made solutions to fulfil individual patient requirements. Bioelectronic medicines and devices are new technology where the patient wears a device and its electrical signal cures certain ailments. Tele pharmacy ensures that the technological advances of telecommunications are also passed on to the patient health care sector. Moreover, it can be said that all these modern developments ensure that the quality of life improves and there comes a better control on the health care costs. Keywords: artificial intelligence, personalized medicines, customized medicines, 3D printing, bioelectronic devices and tele pharmacy

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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